Senior Data Analyst
Wealth Management Practice | Data Engineering & Analytics
DOMAIN
Wealth Management
TECHNICAL CORE
Advanced SQL / STTM
CLOUD ENV
MS Azure (Preferred)
EXPERIENCE
5+ Years Senior Level
Key Objective: We are seeking a highly analytical Senior Data Analyst to lead data discovery, quality profiling, and
Source-to-Target Mapping (STTM) for enterprise wealth management data platforms. This role serves as the critical bridge
between wealth business teams and engineering developers.
Role Overview
As a Senior Data Analyst in our Wealth Management technology practice, you will play a central role in shaping
client and portfolio data solutions. You will be responsible for navigating complex legacy and modern data
structures—including client profiles, accounts, holdings, transactions, performance metrics, and advisory billing
data.
You will perform deep-dive data profiling and pattern analysis using advanced SQL, assess data health and quality,
and author comprehensive Source-to-Target Mapping (STTM) documentation. Crucially, you will act as the
principal functional contact for ETL/Data Engineers, effectively translating business logic into actionable
engineering specifications and facilitating clear walkthroughs.
Primary Responsibilities
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Data Profiling & Pattern Analysis: Execute complex SQL queries across relational databases, data lakes, and
warehouses to analyze data distribution, evaluate data quality, discover data anomalies, and identify underlying
relational patterns.
Source-to-Target Mapping (STTM): Design, author, and maintain robust, granular STTM documents detailing
business rules, field transformations, data types, primary/foreign key relationships, and data pipeline logic.
Developer Collaboration & Bridge: Conduct detailed walkthroughs of mapping documents with engineering
teams (ETL/Data Pipeline developers), clarifying edge cases, data constraints, and business intent to drive
smooth implementation.
Data Quality & Governance: Establish baseline data quality metrics, define data validation rules, and
collaborate with data governance leads to remediate data discrepancies or gaps across financial datasets.
Wealth Management Domain Application: Analyze domain-specific data entities, including household
relationships, investment portfolios, asset classes, custody positions, fee calculations, and trade histories.
Stakeholder Communication: Articulate data insights, structural risks, and mapping dependencies clearly to
both technical developers and non-technical business stakeholders/product owners.
Testing & Acceptance Support: Assist QA and engineering teams during sprint cycles by validating
transformed datasets against original target specifications using customized SQL validation scripts.
Confidential - Wealth Management Practice
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Minimum Qualifications
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Experience: 5+ years of hands-on experience as a Data Analyst, Data Modeler, or Technical Business Analyst
in enterprise data environment initiatives.
Advanced SQL Expertise: Proven mastery in writing complex SQL scripts (multi-table JOINs, CTEs, window
functions, subqueries, and analytical functions) for data extraction and profiling.
STTM Documentation: Demonstrated experience creating explicit, comprehensive Source-to-Target Mappings
(STTM) for ETL/ELT pipelines, reporting, or data warehouse migrations.
Data Quality & Profiling: Strong background in identifying data anomalies, missingness, structural
inconsistencies, and data integrity issues.
Communication Skills: Exceptional verbal and written communication skills with proven experience leading
technical specification reviews with software developers and architects.
Education: Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Finance, or a related
quantitative field.
Preferred Experience & Skills
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Wealth Management Domain Knowledge: Direct experience working with financial, wealth, investment
management, brokerage, or banking data domains (e.g., portfolio management, custodial feeds, advisory
accounts).
MS Azure Cloud Environment: Exposure to or experience working with cloud data platforms on Microsoft
Azure (e.g., Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, or Databricks on Azure).
Modern Data Stacks: Familiarity with modern data modeling concepts (Dimensional, Snowflake, Data Vault)
and orchestration workflows.
Agile/Scrum Framework: Experience working in Agile/Scrum delivery models, managing user stories, and
utilizing tools like Jira or Azure DevOps.
Core Skills & Competencies
Advanced SQL
Source-to-Target Mapping (STTM)
Wealth Management Domain
Jira / Azure DevOps
Data Lineage
Confidential - Wealth Management Practice
Data Profiling & Quality
MS Azure Cloud
Data Pipeline Specification
Developer Communication
Relational Modeling
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